An increasing number of user devices require an increasing amount of user input and often that user input represents alphanumeric information such as, for example, the text of a message or the address of a recipient. At the same time, many user devices, and especially portable user devices, are becoming smaller. Smaller form factors unfortunately reduce the amount of physical space available for the user interface (this tends not only to impact the user input mechanism but also corresponding output mechanisms, memory, and computational resources as also support the overall user interface). As a result, various existing user interfaces tend to be unsatisfactory for one reason or another.
This general dissatisfaction exists essentially regardless of the input modality itself (including keyboards and keypads, speech recognition, handwriting recognition, and more exotic input mechanisms such as those driven by alpha waves and other biological electromagnetic signals and fields). Such approaches tend to be too large and/or require too much in the way of device resources (such as memory or computational capability), and/or are otherwise time consuming, tedious, error prone, and for many users tend to discourage rather than encourage usage. Generally put, present solutions for small user devices often tend to result in a poor user experience, one way or the other, ranging from an inability to properly utilize the input mechanism to an uncomfortably high and fatiguing cognitive load to achieve what amounts to modestly acceptable performance for the input mechanism.
Recognition techniques (or, for some keypad-based input mechanisms, so-called predictive techniques) attempt to address these issues by resolving ambiguous input data into words that presumably a user is likely to input into the device. Such techniques are usually based upon standard models that represent common word usage amongst a sampling study, along with other statistical information (for example the distribution of character shapes for a so-called handwriting recognizer). As a result, these techniques can work well for some users and quite poorly for others. Further, such techniques often require considerable training as the user interface is not sufficiently intuitive to the user. In addition, even users for whom the technique works relatively well often find that the technique does not work equally well for all of their needs (for example, the resultant selections may be relatively useful when supporting creation of a business-related message but relatively unhelpful or even annoying when the user creates more casual correspondence).
As a partial remedy for this concern, it has been suggested that such standard models can be augmented by using an additional dictionary of words that the user must create and customize. While this approach can improve recognition for a given user, the editing process itself can be time consuming, tedious, and otherwise typify some of the same problems that the predictive techniques were initially trying to alleviate. In addition, such solutions tend to be highly consumptive of both memory and processing capacity. These needs can render such techniques ill suited for many portable user devices where these resources are either limited or other corresponding limitations of concern exist (such as respecting a need to minimize current consumption in a portable device).
Simply put, present user input devices and techniques do not provide, especially for small user devices, a suitable and relatively intuitive user input interface that will reduce the time and associated cognitive load required to accurately input alphanumeric information, either for a given input modality or in compatible cooperation with a variety of input modalities.
Skilled artisans will appreciate that elements in the figures are illustrated for simplicity and clarity and have not necessarily been drawn to scale. Also, some components may be shown in reduced number in order to render more clearly an understanding of various embodiments of the present invention.